Pub Date : 2023-01-25DOI: 10.1142/s021969132250045x
T. Idhaya, A. Suruliandi, D. Calitoiu, S. Raja
A gene is a basic unit of congenital traits and a sequence of nucleotides in deoxyribonucleic acid that encrypts protein synthesis. Proteins are made up of amino acid residue and are classified for use in protein-related research, which includes identifying changes in genes, finding associations with diseases and phenotypes, and identifying potential drug targets. To this end, proteins are studied and classified, based on the family. For family prediction, however, a computational rather than an experimental approach is introduced, owing to the time involved in the latter process. Computational approaches to protein family prediction involve two important processes, feature selection and classification. Existing approaches to protein family prediction are alignment-based and alignment-free. The drawback of the former is that it searches for protein signatures by aligning every available sequence. Consequently, the latter alignment-free approach is taken for study, given that it only needs sequence-based features to predict the protein family and is far more efficient than the former. Nevertheless, the sequence-based characteristics taken for study have additional features to offer. There is, thus, a need to select the best features of all. When comes to classification still there is no perfection in classifying the protein. So, a comparison of different approaches is done to find the best feature selection technique and classification technique for protein family prediction. From the study, the feature subset selected provides the best classification accuracy of 96% for filter-based feature selection technique and the random forest classifier.
{"title":"Calibrating the classifier for protein family prediction with protein sequence using machine learning techniques: An empirical investigation","authors":"T. Idhaya, A. Suruliandi, D. Calitoiu, S. Raja","doi":"10.1142/s021969132250045x","DOIUrl":"https://doi.org/10.1142/s021969132250045x","url":null,"abstract":"A gene is a basic unit of congenital traits and a sequence of nucleotides in deoxyribonucleic acid that encrypts protein synthesis. Proteins are made up of amino acid residue and are classified for use in protein-related research, which includes identifying changes in genes, finding associations with diseases and phenotypes, and identifying potential drug targets. To this end, proteins are studied and classified, based on the family. For family prediction, however, a computational rather than an experimental approach is introduced, owing to the time involved in the latter process. Computational approaches to protein family prediction involve two important processes, feature selection and classification. Existing approaches to protein family prediction are alignment-based and alignment-free. The drawback of the former is that it searches for protein signatures by aligning every available sequence. Consequently, the latter alignment-free approach is taken for study, given that it only needs sequence-based features to predict the protein family and is far more efficient than the former. Nevertheless, the sequence-based characteristics taken for study have additional features to offer. There is, thus, a need to select the best features of all. When comes to classification still there is no perfection in classifying the protein. So, a comparison of different approaches is done to find the best feature selection technique and classification technique for protein family prediction. From the study, the feature subset selected provides the best classification accuracy of 96% for filter-based feature selection technique and the random forest classifier.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125550182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-19DOI: 10.1142/s0219691323500066
Siva Prasad Murugan, G. P. Youvaraj
{"title":"Rational Franklin MRA and its Wavelets","authors":"Siva Prasad Murugan, G. P. Youvaraj","doi":"10.1142/s0219691323500066","DOIUrl":"https://doi.org/10.1142/s0219691323500066","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-19DOI: 10.1142/s0219691323500078
S. R. Lyernisha, C. Christopher, S. R. Fernisha
{"title":"Object recognition from enhanced underwater image using optimized deep-CNN","authors":"S. R. Lyernisha, C. Christopher, S. R. Fernisha","doi":"10.1142/s0219691323500078","DOIUrl":"https://doi.org/10.1142/s0219691323500078","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133415833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1142/s0219691323500030
Haibo Yang, Qixiang Yang, Huo-xiong Wu
{"title":"Middle frequency band and remark on Koch-Tataru's iteration space","authors":"Haibo Yang, Qixiang Yang, Huo-xiong Wu","doi":"10.1142/s0219691323500030","DOIUrl":"https://doi.org/10.1142/s0219691323500030","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.1142/s0219691323500029
R. Fazli, Hadi Owlia, R. Sheikhpour
{"title":"A robust method for coherent and non-coherent source number detection using a special Hankel-based covariance matrix","authors":"R. Fazli, Hadi Owlia, R. Sheikhpour","doi":"10.1142/s0219691323500029","DOIUrl":"https://doi.org/10.1142/s0219691323500029","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123826844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.1142/s0219691322500424
Xuefeng Wang
This note designs two kinds of rational wavelet filter banks using three basic bricks: the finite Blaschke product, Bezout polynomial and the symbol of the cardinal B-spline. In orthogonal case, the corresponding wavelets are generalization of Daubechies’ wavelets. The role of the Blaschke product is the adjustment of the peaks of wavelet functions.
{"title":"Rational wavelet filter banks from Blaschke product","authors":"Xuefeng Wang","doi":"10.1142/s0219691322500424","DOIUrl":"https://doi.org/10.1142/s0219691322500424","url":null,"abstract":"This note designs two kinds of rational wavelet filter banks using three basic bricks: the finite Blaschke product, Bezout polynomial and the symbol of the cardinal B-spline. In orthogonal case, the corresponding wavelets are generalization of Daubechies’ wavelets. The role of the Blaschke product is the adjustment of the peaks of wavelet functions.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133543206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-22DOI: 10.1142/s0219691323500017
Youming Liu, Zhentao Zhang
{"title":"Estimation of misclassification rate in the Asymptotic Rare and Weak model with sub-Gaussian noises","authors":"Youming Liu, Zhentao Zhang","doi":"10.1142/s0219691323500017","DOIUrl":"https://doi.org/10.1142/s0219691323500017","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114306685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.1142/s0219691322500448
T. U. Kamble, S. Mahajan
3D image reconstruction using multi-view imaging is widely utilized in several application domains: construction field, disaster management, urban planning, etc. The 3D reconstruction from the multi-view image is still challenging due to the high freedom and inaccurate reconstruction. This research introduces the hybrid deep learning technique for reconstructing the 3D image, in which the C-dual attention layer is proposed for generating the feature map to support the image reconstruction. The proposed 3D image reconstruction uses the encoder–decoder–refiner which is utilized for reconstruction. Initially, the features are extracted from the AlexNet and ResNet-50 features automatically. Then, the proposed C-dual attention layer is utilized for generating the inter-channel and inter-spatial relationship among the features to obtain enhanced reconstruction accuracy. The inter-channel relationship is evaluated using the channel attention layer, and the inter-spatial relationship is evaluated using the spatial attention layer of the encoder module. Here, the features generated by the spatial attention layer are combined to form the feature map in a 2D map. The proposed C-dual attention encoder provides enhanced features that help to acquire enhanced 3D image reconstruction. The proposed method is evaluated based on loss, IoU_3D, and IoU_2D, and acquired the values of 0.0721, 1.25 and 1.37, respectively.
{"title":"3D Image reconstruction using C-dual attention network from multi-view images","authors":"T. U. Kamble, S. Mahajan","doi":"10.1142/s0219691322500448","DOIUrl":"https://doi.org/10.1142/s0219691322500448","url":null,"abstract":"3D image reconstruction using multi-view imaging is widely utilized in several application domains: construction field, disaster management, urban planning, etc. The 3D reconstruction from the multi-view image is still challenging due to the high freedom and inaccurate reconstruction. This research introduces the hybrid deep learning technique for reconstructing the 3D image, in which the C-dual attention layer is proposed for generating the feature map to support the image reconstruction. The proposed 3D image reconstruction uses the encoder–decoder–refiner which is utilized for reconstruction. Initially, the features are extracted from the AlexNet and ResNet-50 features automatically. Then, the proposed C-dual attention layer is utilized for generating the inter-channel and inter-spatial relationship among the features to obtain enhanced reconstruction accuracy. The inter-channel relationship is evaluated using the channel attention layer, and the inter-spatial relationship is evaluated using the spatial attention layer of the encoder module. Here, the features generated by the spatial attention layer are combined to form the feature map in a 2D map. The proposed C-dual attention encoder provides enhanced features that help to acquire enhanced 3D image reconstruction. The proposed method is evaluated based on loss, IoU_3D, and IoU_2D, and acquired the values of 0.0721, 1.25 and 1.37, respectively.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134152783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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